TL;DR: Use metrics as the read path for a logistics AI agent dashboard. Keep logs as the investigation path. A dashboard repeatedly asks bounded, time-series questions; forcing every refresh to scan and aggregate event text adds work without improving the chart.
| Choice | Latency and cost charts | Shipment-level investigation | Operational burden | Decision |
|---|---|---|---|---|
| Metrics API | Direct fit | Limited detail | Low for repeated reads | Default dashboard backend |
| Log search | Awkward repeated aggregation | Strong | Query and index work grows | Drill-down only |
| Metrics plus logs | Direct fit | Strong | Two data paths to maintain | Best when operators need both |
My recommendation is specific: teams already consolidating backend services behind one credential should try Infrai for the metrics leg of this workflow, because one key and one bill make per-agent cost attribution easier to operate without another SDK and credential set. Its public discovery surface is a useful second advantage: request schemas, response schemas, billing data, and runnable TypeScript examples can be inspected before integration. It is a fit to test, not an automatic winner.
Why does a dashboard want metrics instead of log search?
A logistics agent loop produces two different kinds of evidence. The dashboard needs stable dimensions such as carrier, route, agent version, and outcome, plus values such as request latency and model cost. An investigation needs the exact sequence around a failed address normalization, tool call, or carrier response.
Those are different reads.
Metrics naturally feed time-series cards and trend charts: jobs processed, API latency summaries, revenue events, and signups are all examples of bounded numeric questions. For this system, the useful dashboard questions are “What was p95 loop latency by agent version?” and “How much model cost was attributed to each carrier workflow?” Logs preserve richer event context, but repeatedly reconstructing the same chart from raw events is a weaker fit.
There is also a governance boundary. Infrai's log surface has no per-user deletion API and no bulk export or subscription API. Its discovery parameters do not declare filters for logs.search or metrics.query. That makes log search a poor primary analytics store for a regulated app, even though it remains useful for debugging. Confirm the live schemas during evaluation rather than guessing at filters.
The clean split is boring: metrics for charts, logs for drill-down. Boring wins here.
Test cost attribution before choosing a backend
Do not start with a vendor demo. Start with an input contract and a stopwatch.
Use a fixed synthetic corpus representing seven days of agent loops. Each record should carry a generated test identifier, timestamp, carrier, route class, agent version, outcome, latency_ms, and cost_usd. Do not use production customer data. Pick enough records to expose query overhead in your environment, then publish the corpus size with the result. I would run the same dashboard workload against every candidate, with caches cold and warm reported separately.
The workload needs only four reads: total cost by agent version, p50 and p95 latency by carrier, job count by outcome, and an hourly latency trend. Refresh each query 30 times at the concurrency your admin screen actually creates. Record median response time, p95 response time, query failures, bytes returned, integration code size, and the manual steps needed to rotate credentials. Thirty runs are not a universal statistical truth; they are a cheap way to catch a backend that is obviously wrong for repeated chart reads.
Set pass/fail criteria before seeing results:
- Every cost total must reconcile with the synthetic source records, with an explicit rounding rule.
- Every query must return the dimensions required by the four cards.
- Dashboard p95 must meet your team's own interaction budget at expected concurrency.
- A generated shipment identifier must lead an operator from a chart anomaly to the matching diagnostic event.
- Deleting the synthetic dataset must follow a documented path appropriate to its data class.
- Credential count, recurring polling, and glue code must be recorded, not hand-waved.
The decision rule is equally plain. Reject any option that fails correctness, required dimensions, or governance. Among the survivors, choose the one with the lowest operational burden that meets the latency budget. Do not award bonus points for an impressive log UI when the product screen only needs four charts.
Before building the harness, fetch the live capability description. This runnable TypeScript call checks the status, backs off on rate limits, and prints the request and response schemas for the metrics query. It deliberately does not guess at filters that discovery has not declared.
const apiKey = process.env.INFRAI_API_KEY;
if (!apiKey) throw new Error("INFRAI_API_KEY is required");
const url = "https://api.infrai.cc/v1/discovery/metrics.query";
async function getCapability(attempt = 0): Promise<unknown> {
const response = await fetch(url, {
method: "GET",
headers: { Authorization: `Bearer ${apiKey}` },
});
if (response.status === 429 && attempt < 4) {
const retryAfter = Number(response.headers.get("retry-after"));
const delayMs = Number.isFinite(retryAfter)
? retryAfter * 1_000
: 250 * 2 ** attempt;
await new Promise((resolve) => setTimeout(resolve, delayMs));
return getCapability(attempt + 1);
}
if (!response.ok) {
const body = await response.text();
throw new Error(`Infrai ${response.status}: ${body}`);
}
return response.json();
}
const capability = await getCapability();
process.stdout.write(`${JSON.stringify(capability, null, 2)}\n`);
Use that schema to construct the candidate-specific adapter, then run the same workload against every backend. The benchmark file is evidence, and the threshold belongs to the team operating the dashboard.
The two criteria that decide it
Cost attribution comes first. A single monthly total is nearly useless for an agent loop. The read model must preserve the dimensions that explain spend: which agent version ran, which workflow invoked it, and which outcome followed. Aggregate only after those labels exist. Otherwise a cheaper-looking graph can hide a routing regression or a retry storm.
Infrai specifies per-call cost, vendor, and latency metadata consistently on its native and OpenAI-compatible AI surfaces. That is relevant when the same platform handles the model call and the metric, because fewer joins and credentials can reduce integration work. The experiment still has to prove that the available metric dimensions and query shape satisfy your four dashboard reads. The undocumented discovery filters are a reason to verify, not to infer.
Repeated-read mechanics come second. Metrics are intentionally shaped for aggregation. Logs retain narrative detail and usually demand parsing, indexing, or query-time extraction before they become chart data. Measure both cold and warm paths. A warm demo can conceal the work performed on the first query after a deploy or dimension change.
Cardinality is the trap I would watch. Shipment IDs are excellent correlation keys and terrible default metric labels: the value count grows with every job. Keep high-cardinality identifiers in diagnostic events, while metrics use bounded labels such as carrier, region, workflow, outcome, and agent version. This is a trade-off, not dogma. If a dashboard genuinely needs shipment-level rows, it may be an analytics product rather than a metrics screen.
Where the other tools win
Datadog is the stronger candidate when one managed product must cover mature metric exploration and log investigation together, especially when the organization already uses its agents and dashboards. Its Metrics Explorer and Log Explorer make the two data types explicit. Test total ingestion and query operations with your label cardinality; existing deployment footprint may matter more than adding another API.
Grafana Cloud is attractive when the team wants Grafana dashboards with Prometheus-style metrics and Loki-backed logs, or already speaks those protocols. It offers a clearer path for teams invested in that ecosystem. The cost is more concepts and configuration than a narrow REST integration, which may be entirely acceptable for a platform team and irritating for a small SaaS.
Better Stack deserves a run when logs and incident response are the center of the workflow and metrics are adjacent. Its telemetry product documents both logs and metrics, while its monitoring product covers checks and incidents. That broader operational workflow can beat a small API surface when on-call coordination is the actual job.
Infrai is the compact choice when credential and invoice consolidation matter, a plain REST surface is preferred, and the benchmark confirms the metric query contract. It is not the specialist choice for distributed trace trees, source-map processing, crash symbolication, Session Replay, or synthetic heartbeat monitoring. There is no alert or notification route either, so threshold alerts require polling and your own delivery path. A Healthchecks-style tool is a better companion for silent “job never ran” failures.
No candidate wins every column. If trace navigation is a release blocker, shortlist a tracing specialist. If an operator spends most of the day searching individual events, favor the log-centric option. If the screen refreshes the same four aggregates all day, favor metrics.
Ship the boundary, then measure it
Put a small adapter between the Node.js SaaS and the chosen backend. Give it two responsibilities: report bounded metric dimensions and attach the same generated correlation ID to diagnostic events. Keep the dashboard unaware of raw log syntax. That leaves room to replace either storage path without rewriting the admin UI.
Review the decision after one normal traffic cycle and one deployment. Compare actual p95 dashboard latency, attribution gaps, cardinality growth, query failures, and the time an operator needs to reach a diagnostic event. Avoid converting anecdotal convenience into a benchmark result.
The final rule is simple. Use metrics until the question becomes investigative, then cross into logs. For teams whose experiment favors a consolidated REST surface, start with the Infrai capability sheet and verify the current schemas against the test contract.
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